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Journal articles on the topic 'GRAY SCALE IMAGE'

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Author: Grafiati

Published: 11 September 2023

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1

Edan, Salam Jabbar, Maryam Nadhim Rasoul, and Amir A. Aljarrah. "A Proposed Technique for Gray Image Colorization." Webology 19, no. 1 (January 20, 2022): 5099–106. http://dx.doi.org/10.14704/web/v19i1/web19343.

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The colorization of the gray-scale image is the process of using color image (has a similar "mood") to add color to a grayscale image. In this paper, we proposed new approach for colorization gray-scale images depending on Singular Value Decomposition (SVD). We used SVD to add color to a grayscale image by determining the best pixel value in the reference image, based on comparing the first column values from the left singular vector matrix of 3x3 block and the pixel value in the center of block of the gray image with other corresponding values in the reference image. Finally, use the best match to transfer color from color image to gray-scale image. The results of the proposed colorization approach are good and plausible.

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2

Eberly, David, and Jack Lancaster. "On gray scale image measurements." CVGIP: Graphical Models and Image Processing 53, no. 6 (November 1991): 538–49. http://dx.doi.org/10.1016/1049-9652(91)90004-4.

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Eberly, David, Jack Lancaster, and Abdalmajeid Alyassin. "On gray scale image measurements." CVGIP: Graphical Models and Image Processing 53, no. 6 (November 1991): 550–62. http://dx.doi.org/10.1016/1049-9652(91)90005-5.

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4

Kalra, G. S., and Shilpi Singh. "Efficient digital image denoising for gray scale images." Multimedia Tools and Applications 75, no. 8 (February 8, 2015): 4467–84. http://dx.doi.org/10.1007/s11042-015-2484-x.

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5

Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.

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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long short term memory) which is a type of RNN (Recurrent Neural Network) together so that using computer vision computer can recognize the context of an image and display it in natural language like English. Gray Scale Image captioning can give captions for both monochrome and color images. Keywords: Image, Caption, Convolutional Neural Networks, Long Short Term Memory, Recurrent Neural Network

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Tian, Kai, and Jia Rong Shi. "Color Composite of Digital Radiography Image and Region Growing Defect Detection." Applied Mechanics and Materials 380-384 (August 2013): 938–42. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.938.

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DR image in industrial castings is usually a gray scale image and it has difficulty in simultaneously showing the defects within different thickness parts. To address this issue, the floating point type data obtained by the scanning of DR system is firstly converted into three gray scale images by the more gray stretch technique, where different gray scale images contain different thickness defects of the castings. Secondly, those three gray images are synthesized into one color image. The synthesized image contains lots of details of measured castings and its aim is to reduce the loss of image detail caused by the converting procedure. Then region growing model is exploited to segment the defects of images. Finally, experimental results show that the color synthesis method can better segment the casting defects of the different wall thickness.

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Wu, Shuyue, and Jingfang Wang. "A Quantum Pointer Signal Processing Research." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (June 1, 2016): 675. http://dx.doi.org/10.11591/ijeecs.v2.i3.pp675-683.

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<span lang="EN-US">In quantum gray-scale image processing, the storage in quantum states is the color information and the position information According to the advantage of small range of the gray scale in a gray-scale image, a novel storage expression of quantum gray-scale image is proposed and demonstrated in this study. Besides, a new concept of "quantum pointer" is put forward based on the expression. Quantum pointer is the vinculum between the information of gray-scale and position of each pixel in quantum gray-scale images. The feasibility is verified for the proposed quantum pointer, and the properties of bi-direction and sub-block are used, the storing and other operations of quantum gray-scale image are simpler and more convenient. </span>

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Yang, Yu-Guang, Qian-Qian Zhao, and Si-Jia Sun. "Novel quantum gray-scale image matching." Optik 126, no. 22 (November 2015): 3340–43. http://dx.doi.org/10.1016/j.ijleo.2015.08.010.

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Li, Ming Jing, Yu Bing Dong, and Xiao Li Wang. "Research and Development of Non Multi-Scale to Pixel-Level Image Fusion." Applied Mechanics and Materials 448-453 (October 2013): 3621–24. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3621.

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Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a new image. Simple pixel level image fusion method mainly includes the pixel gray value being average or weighted average, pixel gray value being selected large and pixel gray value being selected small, etc. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.

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G, Srinivasa Rao, Sri Krishna A, Mahaboob Basha S, and Prakash Ch. Jeevan. "Object-Based Image Enhancement Technique for Gray Scale Images." International Journal of Advanced Information Technology 4, no. 3 (June 30, 2014): 9–23. http://dx.doi.org/10.5121/ijait.2014.4302.

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11

Hayder, Israa M., Hussain A. Younis, and Hameed Abdul-Kareem Younis. "Digital Image Enhancement Gray Scale Images In Frequency Domain." Journal of Physics: Conference Series 1279 (July 2019): 012072. http://dx.doi.org/10.1088/1742-6596/1279/1/012072.

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12

CHENG, JIN-CHANG, and HON-SON DON. "SEGMENTATION OF BILEVEL IMAGES USING MATHEMATICAL MORPHOLOGY." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (October 1992): 595–628. http://dx.doi.org/10.1142/s0218001492000321.

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This paper presents the results of a study on the use of morphological skeleton transformation to segment gray-scale images into bilevel images. When a bilevel image (such as printed texts and machine tools) is digitized, the result is a gray-scale image due to the point spread function of digitizer, non-uniform illumination and noise. Our method can recover the original bilevel image from the gray-scale image. The theoretical basis of the algorithm is the physical structure of the skeleton set. A connectivity property of the gray-scale skeleton transformation is used to separate and remove the background terrain. The object pixels can then be obtained by applying a global threshold. Experimental results are given.

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Kondo, Takashi, and Xiaohua Zhang. "Coloring of Gray Scale Image Using Image Color Transfer." Journal of The Institute of Image Information and Television Engineers 61, no. 6 (2007): 838–41. http://dx.doi.org/10.3169/itej.61.838.

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14

Chopra, Deepshikha. "Lsb Based Digital Image Watermarking For Gray Scale Image." IOSR Journal of Computer Engineering 6, no. 1 (2012): 36–41. http://dx.doi.org/10.9790/0661-0613641.

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QIAN, KAI, SIQI CAO, and PRABIR BHATTACHARYA. "GRAY IMAGE SKELETONIZATION WITH HOLLOW PREPROCESSING USING DISTANCE TRANSFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (September 1999): 881–92. http://dx.doi.org/10.1142/s0218001499000483.

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This paper presents a gray-scale skeletonization algorithm based on the gray weighted distance transformation (GWDT). The proposed algorithm is conceptually simple and could be used to process nonuniformly distributed gray-scale images. The algorithm can detect the hollows on the gray-scale image, a feature that is often ignored in other skeletonization algorithms, and improves the quality of the skeleton.

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LIN, MIN-HUI, YU-CHEN HU, and CHIN-CHEN CHANG. "BOTH COLOR AND GRAY SCALE SECRET IMAGES HIDING IN A COLOR IMAGE." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 06 (September 2002): 697–713. http://dx.doi.org/10.1142/s0218001402001903.

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In the past, most image hiding techniques have been applied only to gray scale images. Now, many valuable images are color images. Thus, it has become important to be able to apply image-hiding techniques to hide color images. In this paper, our proposed scheme can not only be applied to "a color host image hiding a color secret image", but also to "a color host image hiding a gray scale secret image". Our scheme utilizes the rightmost 3, 2 and 3 bits of the R, G, B channels of every pixel in the host image to hide related information from the secret image. Meanwhile, we utilize the leftmost 5, 6, 5 bits of the R, G, B channels of every pixel in the host image and set the remaining bits as zero to generate a palette. We then use the palette to conduct color quantization on the secret image to convert its 24-bit pixels into pixels with 8-bit palette index values. DES encryption is then conducted on the index values before the secret image is embedded into the rightmost 3, 2, 3 bits of the R, G, B channels of every pixel in the host image. The experimental results show that even under the worst case scenario our scheme guarantees an average host image PSNR value of 39.184 and an average PSNR value of 27.3415 for the retrieved secret image. In addition to the guarantee of the quality of host images and retrieved secret images, our scheme further strengthens the protection of the secret image by conducting color quantization and DES encryption on the secret image in advance. Therefore, our scheme not only expands the application area of image hiding, but is also practical and secure.

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Zhang, Shu Wen, Xiao Ning Zhang, Zhi Yong Wu, and Li Wan Shi. "Research on Asphalt Mixture Injury Digital Image Based on Enhancement and Segmentation Processing Technology." Applied Mechanics and Materials 470 (December 2013): 832–37. http://dx.doi.org/10.4028/www.scientific.net/amm.470.832.

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In order to visually observe the damage evolution of the asphalt mixture, and analyze the damage evolution accurately and quantitatively, the CT image of asphalt mixture image was processed for image enhancing and segmenting, the entire or partial image features can be effectively improved. The dynamics gray-scale range of the image was adjusted by the histogram equalization and provision. Image gray-scale transformation is enhanced by using Matlab software. At the same time, when the algebraic operations and noise filtering is used to process, the mixture image segmentation and boundary identification are achieved. The results show that the contrast and gray scale dynamic range of the gray scale image can be effectively improved by provision and equalization of histogram. The gap, aggregates and binder can be extracted by segmentation technology from asphalt mixture CT images. The real microstructure obtained can accurately reflect the evolution of asphalt damage.

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18

Qomariyah, Nurul, Rahadi Wirawan, Ni Kadek Nova Anggarani, Laili Mardiana, and Kasnawi Alhadi. "Karakteristik Gaharu Grynops Vertegii (Gilg.) Domke Berdasarkan Analisis Sebaran Gray scale Level." EIGEN MATHEMATICS JOURNAL 1, no. 1 (June 28, 2019): 44. http://dx.doi.org/10.29303/emj.v1i1.27.

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Agarwood Grynops Vertegii (Gilg.) Domke is a type of agarwood that is widely cultivated in the NTB area. The economic value of Agarwood is directly proportional to its quality. Color is one of the physical parameters to determine the quality of agarwood. The purpose of this study is to classify Grynops Vertegii (Gilg.) Agarwood based on the distribution of gray scale level using image processing. The method used is image processing based on gray scale level, Agarwood is divided into four classes based on the dominant color, in this study all samples divided into four classes: A, B, C, and D. Image in RGB converted in to gray scale images then processed in histogram to determine the distribution of the degree of gray scale and its intensity. From the results of image processing it can be seen that there is a shift in the peak position, the difference in the gray scale value, and the curve width. Gray scale values in each class A, B, C, and D respectively are 26,35, 62 and 121 with intensity value at peak positions respectively are 43300, 42400, 30350, 31750. Small gray scale values indicated that agarwood has a high black density and vice versa, while the peak position shows the dominant gray scale value in each class.

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Kulkarni, Asawari, and Aparna Junnarkar. "Gray-Scale Image Compression Techniques: A Review." International Journal of Computer Applications 131, no. 13 (December 17, 2015): 22–25. http://dx.doi.org/10.5120/ijca2015907519.

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Ye, Xiufen, Haibo Yang, Chuanlong Li, Yunpeng Jia, and Peng Li. "A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex." Remote Sensing 11, no. 11 (May 29, 2019): 1281. http://dx.doi.org/10.3390/rs11111281.

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When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

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Xue, Guang Hui, Bao Hua Hu, Xin Ying Zhao, Er Meng Liu, and Wei Jian Ding. "Study on Characteristic Extraction of Coal and Rock at Mechanized Top Coal Caving Face Based on Image Gray Scale." Applied Mechanics and Materials 678 (October 2014): 193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.678.193.

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A method on the feature extraction of coal and rock character recognition was mainly put forward based on image gray level distribution and gray scale average value. In order to improve the recognition effects of the image, cropping, gray level transformation, contrast enhancement, median filtering and other preprocessing work were applied individually on the raw image of coal caving and rock caving acquired from mechanized top caving face, then gray histogram of image signal of coal and rock was abstracted and the gray scale mean were calculated. The results shows that (1) the range of gray scale of top coal caving image is mainly between 10-100, and the range of gray scale of top rock caving image mainly between 90-220, (2) the gray scale mean of top rock caving image is around 130, far higher than the gray scale mean of top coal caving image of 66.

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Lakshmi, Muthu, Ganesh Kumar, Bala Subramanian, and Priti Rishi. "MULTI VARIATE NEURO-STATISTICAL SPARSE TRANSFORM FOR GRAY SCALE IMAGES." Latin American Applied Research - An international journal 52, no. 2 (March 25, 2022): 167–72. http://dx.doi.org/10.52292/j.laar.2022.583.

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Abstract-- The main objective of this paper is to examine the performance of Neuro- Statistical sparse transformation function for implementation in a still image vector coding based compression system. This paper discusses the important features of low bit-rate image coding which is based on recent developments in the theory of multivariate nonlinear piecewise polynomial approximation in still images. It combines Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The quality of the reconstructed image is measured objectively using Peak Signal to Noise Ratio. Experimental results show that the proposed image compression system yields higher compression with minimal loss.

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Siqueira, Gustavo Lopes Gomes de, Robson Pequeno de Sousa, Ricardo Alves de Olinda, Carlos Alberto Engelhorn, André Luiz Siqueira da Silva, and Juliana Gonçalves Almeida. "Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images?" Radiologia Brasileira 54, no. 1 (February 2021): 27–32. http://dx.doi.org/10.1590/0100-3984.2019.0138.

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Abstract Objective: To compare ultrasound images of the kidney obtained, randomly or in a controlled manner (standardizing the physical aspects of the ultrasound system), by various professionals and with different devices. Materials and Methods: We evaluated a total of 919 images of kidneys, obtained by five professionals using two types of ultrasound systems, in 24 patients. The images were categorized into four types, by how they were acquired and processed. We compared the gray-scale median and different gray-scale ranges representative of virtual histological tissues. Results: There were statistically significant differences among the five professionals, regardless of the type of ultrasound system employed, in terms of the gray-scale medians for the images obtained (p < 2.2e-16). Analyzing the four categories of images-a totally random image (without any standardization); a standardized image (with fixed values for gain, time gain control, and dynamic range); a normalized version of the random image; and a normalized version of the standardized image-we determined that the random image, even after normalization, differed quite significantly among the professionals (p = 0.006098). The analysis of the normalized version of the standardized image did not differ significantly among the professionals (p = 0.7319). Conclusion: Our findings indicate that a gray-scale analysis of ultrasound images of the kidney performs better when the image acquisition process is standardized and the images undergo a process of normalization.

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Sim, K. S., C. P. Tso, and Y. Y. Tan. "Recursive sub-image histogram equalization applied to gray scale images." Pattern Recognition Letters 28, no. 10 (July 2007): 1209–21. http://dx.doi.org/10.1016/j.patrec.2007.02.003.

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Wang, Lu, Bin Yan, Hong-Mei Yang, and Jeng-Shyang Pan. "Flip Extended Visual Cryptography for Gray-Scale and Color Cover Images." Symmetry 13, no. 1 (December 31, 2020): 65. http://dx.doi.org/10.3390/sym13010065.

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Visual cryptography (VC) has found numerous applications in privacy protection, online transaction security, and voting security, etc. To counteract potential cheating attacks, Lin et al. proposed flip visual cryptography in 2010, where a second secret image can be revealed by stacking one share with a flipped version of another share. The second secret image can be designed as an additional verification mechanism. However, Lin’s scheme produces meaningless shares and is only applicable to binary secret images. It is interesting to explore whether it is possible to extend the flip VC to having cover images (i.e., extended VC) and these cover images are color images. This problem is challenging since too many restricting conditions need to be met. In this paper, we designed a flip VC for gray-scale and color cover images based on constraint error diffusion. We show that it is possible to meet all the constraints simultaneously. Compared with existing schemes, our scheme enjoys the following features: Color cover images, no computation needed for decoding, and no interference from cover image on the recovered secret image.

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Bawaneh, Mohammed J., and Atef A. Obeidat. "A Secure Robust Gray Scale Image Steganography Using Image Segmentation." Journal of Information Security 07, no. 03 (2016): 152–64. http://dx.doi.org/10.4236/jis.2016.73011.

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Fan, Zehua, Desheng Wang, and Nannan Zhang. "Monitoring of Nitrogen Transport Data in Pear Leaves Based on Infrared Spectroscopy." Journal of Chemistry 2022 (June 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/1547582.

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In order to better monitor the data of nitrogen transport in pear leaves, a method based on infrared spectroscopy was proposed. The near-infrared reflection spectrum imaging technology is used to collect the leaf scale spectral image of the target crop. Computer image analysis software is used to process the spectral digital image and extract the spectral data. After statistical analysis, the data are selected as variables. Combined with the chemical analysis test results, the crop nutrition detection model is established, and the conclusion is drawn. The experimental results show that the band gray data involved in the model are scaled and reorganized according to the coefficient proportion by using ENVI through the band calculation command. The final gray image, the original image, and the gray image in the process default to the three-channel analog image of the band (the wavelengths of the bands are 1446, 1373, and 1304 nm, respectively); 944 nm gray image; 1043 nm gray scale image; 1662 nm gray image; (0.102R944 +0.103R1 043 +0.206R1662)/(0.102 + 0.103 + 0.206) grayscale image with signal scaling according to the scale of model coefficient. It is proved that infrared spectroscopy can effectively monitor the data of nitrogen transport in pear leaves.

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Kim, Sang-Yup, and Seong-Whan Lee. "Gray-Scale Nonlinear Shape Normalization Method for Handwritten Oriental Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 01 (February 1998): 81–95. http://dx.doi.org/10.1142/s0218001498000075.

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In general, nonlinear shape normalization methods for binary images have been used in order to compensate for the shape distortions of handwritten characters. However, in most document image analysis and recognition systems, a gray-scale image is first captured and digitized using a scanner or a video camera, then a binary image is extracted from the original gray-scale image using a certain extraction technique. This binarization process may remove some useful information of character images such as topological features, and introduce noises to character background. These errors are accumulated in nonlinear shape normalization step and transferred to the following feature extraction or recognition step. They may eventually cause incorrect recognition results. In this paper, we propose nonlinear shape normalization methods for gray-scale handwritten Oriental characters in order to minimize the loss of information caused by binarization and compensate for the shape distortions of characters. Two-dimensional linear interpolation technique has been extended to nonlinear space and the extended interpolation technique has been adopted in the proposed methods to enhance the quality of normalized images. In order to verify the efficiency of the proposed methods, the recognition rate, the processing time and the computational complexity of the proposed algorithms have been considered. The experimental results demonstrate that the proposed methods are efficient not only to compensate for the shape distortions of handwritten Oriental characters but also to maintain the information in gray-scale Oriental characters.

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Jiang, Bo, Wanxu Zhang, Jian Zhao, Yi Ru, Min Liu, Xiaolei Ma, Xiaoxuan Chen, and Hongqi Meng. "Gray-Scale Image Dehazing Guided by Scene Depth Information." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7809214.

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Combined with two different types of image dehazing strategies based on image enhancement and atmospheric physical model, respectively, a novel method for gray-scale image dehazing is proposed in this paper. For image-enhancement-based strategy, the characteristics of its simplicity, effectiveness, and no color distortion are preserved, and the common guided image filter is modified to match the application of image enhancement. Through wavelet decomposition, the high frequency boundary of original image is preserved in advance. Moreover, the process of image dehazing can be guided by the image of scene depth proportion directly estimated from the original gray-scale image. Our method has the advantages of brightness consistency and no distortion over the state-of-the-art methods based on atmospheric physical model. Particularly, our method overcomes the essential shortcoming of the abovementioned methods that are mainly working for color image. Meanwhile, an image of scene depth proportion is acquired as a byproduct of image dehazing.

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He, Xiao Qin, Jin Jun Li, and Xiao Yan Li. "Multi-Scale Stereo Analysis Based on Local Multi-Model Monogenic Image Feature Descriptors." Advanced Materials Research 433-440 (January 2012): 853–59. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.853.

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A multi-scale method based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal and local mean colors in the multi-scale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Because the proposed feature descriptors contain local geometric, structure and color information, it is robust against noise and brightness change in feature matching and 3D reconstruction. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.

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Li, Jinjun, Hong Zhao, Chengying Shi, and Xiang Zhou. "A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space." Mathematical Problems in Engineering 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/202653.

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A stereo similarity function based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal, and local mean colors in the multiscale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation, and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.

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Huang, Zhi Kai, Xing Wang Zhang, Wei Zhong Zhang, and Ling Ying Hou. "A New Embossing Method for Gray Images Using Kalman Filter." Applied Mechanics and Materials 39 (November 2010): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amm.39.488.

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In this paper, we propose a new embossing algorithm for gray images using Kalman filter. First, a 2D gray image is first converted to a one dimension vector; those vectors could be considered as a one-dimension discrete-time signal. Then, the performance of image filtering using Kalman filter for image is studied and according to its results, Canny edge detection operators are investigated to find edge map in a gray scale image. Finally, enhance contrast using histogram equalization has been applied. Compared with other conventional embossing method for images, it is an impressive experimental result using our proposed algorithm for gray image embossing. Practical results show that this algorithm can be exploited in different fields such as image pattern recognition.

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Zeng Fanfeng, and Shang Wensong. "Image Gray-Scale Enhancement Algorithm Based on Interception." Journal of Convergence Information Technology 7, no. 23 (December 31, 2012): 501–7. http://dx.doi.org/10.4156/jcit.vol7.issue23.59.

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34

R, Reka. "DIRECTIONAL THRESHOLDING ALGORITHM FOR GRAY SCALE IMAGE SEGMENTATION." International Journal of Advances in Signal and Image Sciences 4, no. 1 (June 28, 2018): 23. http://dx.doi.org/10.29284/ijasis.4.1.2018.23-29.

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35

Rosyadi, Imron. "Gray-Scale Image Colorization Using Various Affinity Functions." Dinamika Rekayasa 8, no. 1 (February 4, 2012): 7. http://dx.doi.org/10.20884/1.dr.2012.8.1.52.

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<p>In this paper, we have proposed, implemented, and compared some affinity functions for an image colorization algorithm. The colorization qualityof the proposed affinityfunctions was just slightly behind the original functions, while one of the proposed functions performed faster than the original affinity function. We also implemented the colorization algorithm for coloring an Indonesian historical image.</p>

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Anastassiou, D., W. Pennebaker, and J. Mitchell. "Gray-Scale Image Coding for Freeze-Frame Videoconferencing." IEEE Transactions on Communications 34, no. 4 (April 1986): 382–94. http://dx.doi.org/10.1109/tcom.1986.1096538.

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Yuan, Liying, Junfeng Wu, and Shuangquan Li. "Improved Wavelet Threshold for Gray Scale Image Denoising." International Journal of Signal Processing, Image Processing and Pattern Recognition 7, no. 3 (June 30, 2014): 45–52. http://dx.doi.org/10.14257/ijsip.2014.7.3.05.

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Naseri, Mosayeb, Mona Abdolmaleky, Fariborz Parandin, Negin Fatahi, Ahmed Farouk, and Reza Nazari. "A New Quantum Gray-Scale Image Encoding Scheme." Communications in Theoretical Physics 69, no. 2 (February 2018): 215. http://dx.doi.org/10.1088/0253-6102/69/2/215.

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许, 志磊. "Tongue Image Classification Based on Gray-scale Difference." Computer Science and Application 10, no. 02 (2020): 190–99. http://dx.doi.org/10.12677/csa.2020.102020.

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A. Jasim, Abbas. "Gray Scale Image Hiding Using Wavelet Packet Transform." Iraqi Journal for Electrical And Electronic Engineering 5, no. 1 (December 28, 2009): 51–59. http://dx.doi.org/10.33762/eeej.2009.54940.

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Acevedo, Mar�a Elena, Jos� �ngel Mart�nez, Marco Antonio Acevedo, and Cornelio Ya�ez. "Morphological Associative Memories for Gray-Scale Image Encryption." Applied Mathematics & Information Sciences 8, no. 1 (January 1, 2014): 127–34. http://dx.doi.org/10.12785/amis/080115.

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Liu, Liren, Bo Cui, and Liying Zhao. "Optical cellular fuzzy logic gray-scale image processor." Optics Communications 107, no. 5-6 (May 1994): 453–60. http://dx.doi.org/10.1016/0030-4018(94)90363-8.

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Honda, Hiroyuki, Miki Haseyama, and Hideo Kitajima. "Image representation through gray-scale iterated function systems." Systems and Computers in Japan 27, no. 9 (1996): 55–62. http://dx.doi.org/10.1002/scj.4690270906.

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Abbadi, Nidhal K. El, and Eman Saleem Razaq. "Automatic gray images colorization based on lab color space." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1501. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1501-1509.

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<p>The colorization aim to transform a black and white image to a color image. This is a very hard issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image. The reference image converted to Lab color space, while the gray scale image normalized according to the lightness channel L. the gray image concatenate with both a, and b channels before converting to RGB image. The results were promised compared with other methods.</p>

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Nayak, Nihar Ranjan, Bikram Keshari Mishra, Amiya Kumar Rath, and Sagarika Swain. "A Time Efficient Clustering Algorithm for Gray Scale Image Segmentation." International Journal of Computer Vision and Image Processing 3, no. 1 (January 2013): 22–32. http://dx.doi.org/10.4018/ijcvip.2013010102.

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The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms – the classical K-Means, a modified Watershed segmentation as proposed by A. R. Kavitha et al., (2010) and their proposed Improved Clustering method normally used for gray scale image segmentation. The authors have analyzed the performance measure which affects the result of gray scale segmentation by considering three very important quality measures that is – Structural Content (SC) and Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives remarkable consequence for the computed values of SC, RMSE and PSNR as compared to K-Means and modified Watershed segmentation. In addition to this, the end result of segmentation by means of the Proposed technique reduces the computational time as compared to the other two approaches irrespective of any input images.

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Oliveira, Matheus Lima, Marcela Lacerda Vieira, Adriana Dibo Cruz, Frab Norberto Bóscolo, and Solange Maria de Almeida. "Gray scale inversion in digital image for measurement of tooth length." Brazilian Dental Journal 23, no. 6 (2012): 703–6. http://dx.doi.org/10.1590/s0103-64402012000600013.

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The aim of this study was to assess the applicability of gray scale inversion in performing digital linear endodontic measurements. Standardized digital images were taken of 40 extracted human permanent single-rooted teeth with K-files size 15 placed at different lengths in the root canal, using the CDR Wireless® digital system. All images were treated with Positive, a digital tool that inverts the grayscale value. Eight radiologists measured the distance between the lower limit of the rubber stop and the tip of the file on both original and positive images. After 15 days, they repeated this procedure in 50% of the image samples. The intra- and inter-examiner agreement was analyzed by intraclass correlation test, and the comparison between the two image groups and the actual lengths by the Student's t-test (α=0.05). Intra- and inter-examiner evaluation showed high reproducibility (r>0.95) of both original and positive images. No statistically significant differences (p>0.05) were found between measurements performed on original and positive images, but both differed significantly from the actual lengths (p<0.05). Gray scale inversion has low applicability in measuring endodontic files, since it did not improve the accuracy of measurements.

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Wang, Jin, Yifei Cui, Hao Wang, Mohammad Ikbal, and Mohammad Usama. "Analysis of Extraction Algorithm for Visual Navigation of Farm Robots Based on Dark Primary Colors." International Journal of Agricultural and Environmental Information Systems 12, no. 2 (April 2021): 61–72. http://dx.doi.org/10.4018/ijaeis.20210401.oa5.

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In order to quickly extract the visual navigation line of farmland robot, an extraction algorithm for dark primary agricultural machinery is proposed. The application of dark primary color principle in new farmland is made clearer by gray scale method, and the soil and crops are obviously separated, and the image processing technology of visual navigation line image of farmland is realized. In binary filtering of gray scale images, the maximum interclass variance method and morphological method are used respectively. The researchers use vertical projection method and least square method to the farmland interval extracted by navigation line. The farmland that needs the guide line image will be accurately located. It is found that the visual navigation extraction algorithm of farmland robot is widely used in the image extraction of navigation lines of various farmland roads and scenes compared with the traditional gray scale algorithm. Image processing has the advantages of clearer image processing.

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LIU, HAIHUA, ZHOUHUI CHEN, and CHANGSHENG XIE. "MULTISCALE MORPHOLOGICAL WATERSHED SEGMENTATION FOR GRAY LEVEL IMAGE." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 04 (December 2006): 627–41. http://dx.doi.org/10.1142/s0219691306001506.

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Multiscale image analysis has been used successfully in a number of applications to segment image features according to their relative scale. In this paper, we present a new framework for the hierarchical segmentation of gray level image. The proposed scheme comprises a nonlinear scale-space and morphological gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image. The scale-space is based on morphological anisotropic diffusion that uses reconstruction morphological operators. Furthermore, we introduce the method to reconstruct morphological operators and the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of image including natural scenes.

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Thirugnanam, Mythili, and S. Margret Anouncia. "Evaluating the performance of various segmentation techniques in industrial radiographs." Cybernetics and Information Technologies 14, no. 1 (March 1, 2014): 161–71. http://dx.doi.org/10.2478/cait-2014-0013.

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Abstract At present, image processing concepts are widely used in different fields, such as remote sensing, communication, medical imaging, forensics and industrial inspection. Image segmentation is one of the key processes in image processing key stages. Segmentation is a process of extracting various features of the image which can be merged or split to build the object of interest, on which image analysis and interpretation can be performed. Many researchers have proposed various segmentation algorithms to extract the region of interest from an image in various domains. Each segmentation algorithm has its own pros and cons based on the nature of the image and its quality. Especially, extracting a region of interest from a gray scale image is incredibly complex compared to colour images. This paper attempts to perform a study of various widely used segmentation techniques in gray scale images, mostly in industrial radiographic images that would help the process of defects detection in non-destructive testing.

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Farhan Khan, Muhammad, Syed Muhammad Ghazanfar Monir, and Imran Naseem. "A Novel Zero-Watermarking Based Scheme for Copyright Protection of Gray scale Images." July 2019 38, no. 3 (July 1, 2019): 627–40. http://dx.doi.org/10.22581/muet1982.1903.09.

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Zero-watermarking of digital images is a powerful method with respect to transparency in the watermarked image. However, robustness is still a challenging characteristic for researchers. The proposed method of zero-watermarking provides a novel solution for increasing robustness by obtaining resident features of gray scale image that are robust against common signal processing operations. The proposed solution is based on image scanning to produce NDD (Neighboring Distance Difference) profile. This scheme is used to extract image features for generating redundancy binary profile with the help of image scanning and identification of robust image areas for embedding a binary watermark. Redundant areas from binary profile show perceptually insignificant regions of gray scale image according to human visual system. Resident features from robust areas of image are collected to generate the zero watermarking binary key image using reversible XOR operation. The binary key is used for extraction of binary watermark. Experimental results of the proposed method have been compared with the results of various zero-watermarking schemes as well as traditional watermarking methods and found much better at slightly higher computational cost. The comparison analysis for testing robustness has been carried out against image processing attacks like Gaussian filtering, block average filtering, motion blur filtering, image resizing, image rotation, image compression and cropping. For each attack maximum correlated watermark from the set of recovered watermarks is selected to evaluate the performance of proposed zero-watermarking scheme. It has been recorded that perfect matching is observed between original and extracted watermarks for a number of signal processing attacks.

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